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A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel

Natalia Kuznetsova1,2, Aleksandr Suvorov1,2, Daria Gognieva1,3

  • 1Institute of Personalized Cardiology of The Center "Digital Biodesign and Personalized Healthcare" of Biomedical Science and Technology Park, Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenovskiy University), 19991 Moscow, Russia.

Diagnostics (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning model analyzes single-channel electrocardiograms (ECG) to screen for left ventricular systolic dysfunction. This simple, non-invasive method shows high accuracy, aiding early detection without medical staff.

Keywords:
ECGartificial intelligenceleft ventricular systolic dysfunctionmachine learningspectral analysis

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning in Healthcare

Background:

  • Diagnosing heart failure is complex and symptoms are often non-specific, limiting screening applicability.
  • A simple, non-invasive screening method for systolic heart dysfunction using readily available biosignals is needed.
  • Single-channel electrocardiogram (ECG) analysis offers a potential solution for accessible cardiac screening.

Purpose of the Study:

  • To develop a machine learning-based screening model for left ventricular systolic dysfunction.
  • To analyze single-channel ECG parameters for detecting reduced left ventricular ejection fraction (LVEF).
  • To create a screening tool that does not require medical staff involvement.

Main Methods:

  • Inclusion of 624 patients (18-90 years) undergoing echocardiography and single-channel I-lead ECG.
  • Determination of left ventricle ejection fraction (LV EF) using the BIPLANE Simpson method.
  • Advanced signal processing and machine learning algorithms (Lasso regression, Extra Trees) applied to ECG data.

Main Results:

  • Lasso regression achieved 79.2% sensitivity and 81.7% specificity for LVEF <52% (men) / <54% (women) (AUC=0.849).
  • Extra Trees model showed 83.1% sensitivity and 82.7% specificity for LVEF <40% (AUC=0.972).
  • External validation on 600 patients demonstrated 98% accuracy, 98.4% specificity, and 93.5% sensitivity.

Conclusions:

  • Machine learning analysis of single-channel ECG parameters offers high diagnostic accuracy for screening left ventricular systolic dysfunction.
  • The developed model shows potential for a simple, effective, and non-invasive method for early detection of cardiac issues.
  • Modern signal processing and AI technologies can significantly enhance cardiovascular screening capabilities.